Pedestrian drone dataset. Introduced in our CVPR 2016 submission "Forecasting Social Navigation in Crowded Complex Scenes", the Stanford Aerial Pedestrian Dataset consists of annotated videos of pedestrians, bikers, skateboarders, cars, buses, and golf carts navigating eight unique scenes on the Stanford University campus. The benchmark dataset consists of 288 video clips formed by 261,908 frames and 10,209 static images, captured by various drone-mounted cameras, covering a wide range of aspects including location (taken from 14 different cities separated by thousands of kilometers in China), environment (urban and country), objects (pedestrian, vehicles Jan 20, 2026 · VisDrone Dataset The VisDrone Dataset is a large-scale benchmark created by the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China. Oct 1, 2023 · The aim was to generate a large-scale continuous data set including various interactions between classical human-driven cars and automated vehicles as well as active mobility users with human MPR Drone dataset is not a traditional person person re-identification dataset with images captured across a camera network. The Stanford Aerial Pedestrian Dataset: Consists of annotated videos of pedestrians, bikers, skateboarders, cars, buses, and golf carts navigating eight unique scenes on the Stanford University campus. Instead, it is collected by a flying drone in both indoor and outdoor environment. This dataset simplifies the VisDrone2019 dataset by merging classes into compact categories for better classification performance. Nov 26, 2020 · This paper announces the free availability of the P-DESTRE dataset, the first of its kind to provide video/UAV-based data for pedestrian long-term re-identification research, with ID annotations Oct 6, 2023 · We evaluate the proposed real-time on-drone pedestrian tracker with real-world drone-captured videos in the DNA+Drone dataset [15]. The dataset provides time-resolved physical fields, along with STL meshes and structured natural language prompts for text-to-geometry synthesis. By identifying pedestrian movements - for example, detecting unusual behaviors or tracking a person across multiple camera views - it can aid in ensuring public safety. We’re on a journey to advance and democratize artificial intelligence through open source and open science. eala ntgjan fjsbbzh nuerklip rsxexk nae fuvw jniaj joe alkfc